Long Jin

13.8k total citations · 2 hit papers
388 papers, 9.6k citations indexed

About

Long Jin is a scholar working on Control and Systems Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Long Jin has authored 388 papers receiving a total of 9.6k indexed citations (citations by other indexed papers that have themselves been cited), including 146 papers in Control and Systems Engineering, 121 papers in Artificial Intelligence and 59 papers in Computer Vision and Pattern Recognition. Recurrent topics in Long Jin's work include Robotic Mechanisms and Dynamics (70 papers), Neural Networks and Applications (69 papers) and Adaptive Control of Nonlinear Systems (36 papers). Long Jin is often cited by papers focused on Robotic Mechanisms and Dynamics (70 papers), Neural Networks and Applications (69 papers) and Adaptive Control of Nonlinear Systems (36 papers). Long Jin collaborates with scholars based in China, United States and Hong Kong. Long Jin's co-authors include Shuai Li, Yunong Zhang, Xin Luo, Mei Liu, Bolin Liao, Zhijun Zhang, Lin Xiao, Yinyan Zhang, Zhengtai Xie and Zhongbo Sun and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Automatic Control and Journal of Agricultural and Food Chemistry.

In The Last Decade

Long Jin

362 papers receiving 9.4k citations

Hit Papers

Gradient-Based Differential Neural-Solution to Time-Depen... 2022 2026 2023 2024 2022 2024 25 50 75 100

Peers

Long Jin
Comparison fields: 5 of 178
  • Control and Systems Engineering 4.8k
  • Artificial Intelligence 3.2k
  • Computer Vision and Pattern Recognition 1.9k
  • Statistical and Nonlinear Physics 936
  • Biomedical Engineering 890
Replace Yunong Zhang with:
Yunong Zhang China
Radu‐Emil Precup Romania
Meng Joo Er Singapore
S.A. Billings United Kingdom
Yeng Chai Soh Singapore
Vladimir Stojanović Serbia
Zhijun Zhang China
M. Vidyasagar India
Yi Chai China
Badong Chen China
Yunong Zhang China View profile →
Citations per field, relative to Long Jin
Long Jin · 1×
Citations per year, relative to Long Jin
Long Jin · 1×

Countries citing papers authored by Long Jin

Since Specialization
Citations

This map shows the geographic impact of Long Jin's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Long Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Long Jin more than expected).

Fields of papers citing papers by Long Jin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Long Jin. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Long Jin. The network helps show where Long Jin may publish in the future.

Co-authorship network of co-authors of Long Jin

This figure shows the co-authorship network connecting the top 25 collaborators of Long Jin. A scholar is included among the top collaborators of Long Jin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Long Jin. Long Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 1
2 2
3 29
4 1
5 24
6 2
7 14
8 3
9 4
10 17
11 4
12 13
13 4
14 12
15 10
16
Gradient-Based Differential Neural-Solution to Time-Dependent Nonlinear Optimization breakdown →
112
17 10
18 44
19 42
20 66

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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2026